Confidence estimation in computer vision.
Estimating the confidence (or, complementary, the uncertainty) has been object of study in classical computer vision problems, such as optical flow [1]} or stereo [2]}. For the former task, we can distinguish between model-inherent methods [3]}, [4]}, [5]}, that are part of the flow estimation model itself, and ad-hoc approaches [6]}, [7]} that process already estimated optical flow maps. For the latter task, a similar distinction can be found in the literature, with methods processing the cost volume [8]} or already estimated disparity maps [9]}, [10]}.
In deep learning, uncertainty estimation has been studied as well [11]} and applied to specific tasks such as optical flow [12]}, single image depth estimation [13]}, visual odometry [14]}, semantic segmentation of LiDAR pointclouds [15]} and multi-task learning [16]} as well.
However, prior works, in general, require some supervision from additional data, such as ground-truth labels for the specific task over which uncertainty is modeled [12]} or additional images in case of self-supervision [13]}. In contrast, in our case, we source supervision from the raw input data alone.
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